Approval strategies defined using (REF ) differ from the optimistic mirror descent algorithm [1]} in two ways.
First, we scale the estimated risk bound \(\xi ^\top M_{t}\)  by hyperparameter \(\eta _2\) , whereas the original formulation fixed \(\eta _2\)  to 1.
By allowing for large values of \(\eta _2\) , our family of approval strategies spans a wider range of optimism, including those that would only be suitable for stationary settings.
Second, the added constraints disallow approval of modifications that performed poorly at previous time points.
In contrast, the original formulation takes a less aggressive approach and only down-weights such models.
However, we find that removing poor-performing modifications tends to be more effective in practice, since model performance is usually worse at later time points, not better.
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